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Sampling

101 papers

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Papers

Representative Demonstration Selection for In-Context Learning with Two-Stage Determinantal Point Process EMNLP 2023
Epsilon Sampling Rocks: Investigating Sampling Strategies for Minimum Bayes Risk Decoding for Machine Translation EMNLP 2023
Improving multiple-try Metropolis with local balancing JMLR 2023
Conformal Frequency Estimation with Sketched Data NIPS 2022
A Quadrature Rule combining Control Variates and Adaptive Importance Sampling NIPS 2022
Constrained Langevin Algorithms with L-mixing External Random Variables NIPS 2022
An Analysis of Ensemble Sampling NIPS 2022
Natural gradient enables fast sampling in spiking neural networks NIPS 2022
Perfect Sampling from Pairwise Comparisons NIPS 2022
Top Two Algorithms Revisited NIPS 2022
On Scalable Testing of Samplers NIPS 2022
Continuously Tempered PDMP samplers NIPS 2022
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent NIPS 2022
Random vs. Best-First: Impact of Sampling Strategies on Decision Making in Model-Based Diagnosis AAAI 2022
Learning Influence Adoption in Heterogeneous Networks AAAI 2022
IS-Count: Large-Scale Object Counting from Satellite Images with Covariate-Based Importance Sampling AAAI 2022
Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees AAAI 2022
A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract) AAAI 2022
Probing BERT’s priors with serial reproduction chains ACL 2022
IQDet: Instance-Wise Quality Distribution Sampling for Object Detection CVPR 2021
A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization ICML 2021
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning ICML 2021
Stein Self-Repulsive Dynamics: Benefits From Past Samples NIPS 2020
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm NIPS 2020
Gradient Estimation with Stochastic Softmax Tricks NIPS 2020